{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def deal_data(path_file):\n",
    "    path_name = path_file.split(\"\\\\\")[-1]\n",
    "    print(f\"处理：{path_name}\")\n",
    "    df1 = pd.read_excel(path_file, skiprows=1)\n",
    "    df1_name = df1.columns.tolist()\n",
    "    all_ = np.sum(df1[df1_name[1]].notnull())\n",
    "    ok_ , counts = [0]*4, 0\n",
    "    for i in range(4):\n",
    "        ok_[i] = np.sum(df1[df1_name[3+i]].notnull())\n",
    "        counts += ok_[i]\n",
    "    ng = np.sum(df1[df1_name[7]].notnull())\n",
    "    buliang = np.sum(df1[df1_name[8]].notnull())\n",
    "    bool_ = all_ == counts\n",
    "    print(f\" \\\n",
    "       没有踢除'不良明细'时的数据统计：\\n \\\n",
    "       总数: {all_} \\n\\\n",
    "       {df1_name[3]}: {ok_[0]}  \\n \\\n",
    "       {df1_name[4]}: {ok_[1]}  \\n\\\n",
    "       {df1_name[5]}: {ok_[2]}  \\n \\\n",
    "       {df1_name[6]}: {ok_[3]}   \\n \\\n",
    "       {df1_name[7]}: {ng}  \\n \\\n",
    "       {df1_name[8]}: {buliang} \\n \\\n",
    "       数据是否正确： {bool_} \\n \\\n",
    "     \")\n",
    "    \n",
    "    del_, okk_, counts = [0]*4, [0]*4, 0\n",
    "    for i in range(4):\n",
    "        del_[i] = np.sum(df1[df1_name[3+i]].notnull() & df1[df1_name[8]].notnull())\n",
    "        okk_[i] = ok_[i] - del_[i]\n",
    "        counts += okk_[i]\n",
    "    all_2 = counts + ng\n",
    "    print(f\" \\\n",
    "       踢除'不良明细'时的数据统计：\\n \\\n",
    "       总数：all_2 \\n \\\n",
    "       三边无，COG侧ND5%OK: {okk_[0]}  \\n \\\n",
    "       三边ND8% OK,COG侧ND5% OK: {okk_[1]} \\n\\\n",
    "       三边ND6% OK,COG侧ND5%OK: {okk_[2]} \\n \\\n",
    "       四边ND5% OK: {okk_[3]}  \\n \\\n",
    "     \")\n",
    "          \n",
    "    name = [df1_name[3], df1_name[4], df1_name[5], df1_name[6], df1_name[7], df1_name[8], \"总数\",\"数据是否正确\"]\n",
    "    col1 = [ok_[0], ok_[1], ok_[2], ok_[3], ng, buliang,all_, bool_]\n",
    "    col2 = [okk_[0], okk_[1], okk_[2], okk_[3], ng, buliang, all_2, bool_]\n",
    "    df2 = pd.DataFrame({\"统计类别\":name,\"没有踢除 不良明细\":col1, \"踢除 不良明细\":col2})\n",
    "    df2.to_csv(f\"./result/{path_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CHJ单屏0.6T漏光数据(1).xlsx'"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "paths = glob.glob(\"./data/*xlsx\")\n",
    "paths[0].split(\"\\\\\")[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "处理：CHJ单屏0.6T漏光数据(1).xlsx\n",
      "        没有踢除 不良明细 时候的数据统计：\n",
      "        总数: 51 \n",
      "       三边无，COG侧ND5%OK: 10  \n",
      "        三边ND8% OK,COG侧ND5% OK: 40  \n",
      "       三边ND6% OK,COG侧ND5%OK: 0  \n",
      "        四边ND5% OK: 0   \n",
      "        ND5% NG: 0  \n",
      "        不良明细: 2 \n",
      "        数据是否正确： False \n",
      "      \n",
      "        踢除 不良明细 时候的数据统计：\n",
      "        总数：all_2 \n",
      "        三边无，COG侧ND5%OK: 10  \n",
      "        三边ND8% OK,COG侧ND5% OK: 38 \n",
      "       三边ND6% OK,COG侧ND5%OK: 0 \n",
      "        四边ND5% OK: 0  \n",
      "      \n"
     ]
    }
   ],
   "source": [
    "paths = glob.glob(\"./data/*xlsx\")\n",
    "for p in paths:\n",
    "    deal_data(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "处理：data_raw.xlsx, sheet:test1\n",
      "        没有踢除'不良明细'时的数据统计：\n",
      "        总数: 7 \n",
      "       tt1: 5  \n",
      "        tt2: 2  \n",
      "       tt3: 0  \n",
      "        tt4: 0   \n",
      "        ttt5: 0  \n",
      "        不良明细: 0 \n",
      "        数据是否正确： True \n",
      "      \n",
      "        踢除'不良明细'时的数据统计：\n",
      "        总数：all_2 \n",
      "        tt1: 5  \n",
      "        tt2: 2 \n",
      "       tt3: 0 \n",
      "        tt4: 0  \n",
      "      \n",
      "处理：data_raw.xlsx, sheet:test2\n",
      "        没有踢除'不良明细'时的数据统计：\n",
      "        总数: 7 \n",
      "       tt1: 4  \n",
      "        tt2: 2  \n",
      "       tt3: 0  \n",
      "        tt4: 0   \n",
      "        ttt5: 0  \n",
      "        不良明细: 0 \n",
      "        数据是否正确： False \n",
      "      \n",
      "        踢除'不良明细'时的数据统计：\n",
      "        总数：all_2 \n",
      "        tt1: 4  \n",
      "        tt2: 2 \n",
      "       tt3: 0 \n",
      "        tt4: 0  \n",
      "      \n"
     ]
    }
   ],
   "source": [
    "!python data_count.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "sheet = pd.read_excel(\"./data/CHJ单屏0.6T漏光数据(1).xlsx\",sheet_name=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.6T'"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(sheet.keys())[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"./data/CHJ单屏0.6T漏光数据(1).xlsx\",sheet_name='0.6T', skiprows=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>产品序号</th>\n",
       "      <th>ID</th>\n",
       "      <th>编号</th>\n",
       "      <th>三边无，COG侧ND5%OK</th>\n",
       "      <th>三边ND8% OK,COG侧ND5% OK</th>\n",
       "      <th>三边ND6% OK,COG侧ND5%OK</th>\n",
       "      <th>四边ND5% OK</th>\n",
       "      <th>ND5% NG</th>\n",
       "      <th>不良明细</th>\n",
       "      <th>显示指标</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AS157HLT-L10-28P120221021000613</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>完成</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AS157HLT-L10-28P120221021000621</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>完成</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   产品序号                               ID   编号  三边无，COG侧ND5%OK  \\\n",
       "0   NaN  AS157HLT-L10-28P120221021000613  1.0             NaN   \n",
       "1   NaN  AS157HLT-L10-28P120221021000621  2.0             NaN   \n",
       "\n",
       "   三边ND8% OK,COG侧ND5% OK  三边ND6% OK,COG侧ND5%OK  四边ND5% OK  ND5% NG 不良明细 显示指标  \n",
       "0                    1.0                   NaN        NaN      NaN  NaN   完成  \n",
       "1                    1.0                   NaN        NaN      NaN  NaN   完成  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>产品序号</th>\n",
       "      <th>ID</th>\n",
       "      <th>编号</th>\n",
       "      <th>三边无，COG侧ND5%OK</th>\n",
       "      <th>三边ND8% OK,COG侧ND5% OK</th>\n",
       "      <th>三边ND6% OK,COG侧ND5%OK</th>\n",
       "      <th>四边ND5% OK</th>\n",
       "      <th>ND5% NG</th>\n",
       "      <th>不良明细</th>\n",
       "      <th>显示指标</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AS157HLT-L10-28P120221021000613</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>完成</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>AS157HLT-L10-28P120221021000621</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>完成</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   产品序号                               ID   编号  三边无，COG侧ND5%OK  \\\n",
       "0   NaN  AS157HLT-L10-28P120221021000613  1.0             NaN   \n",
       "1   NaN  AS157HLT-L10-28P120221021000621  2.0             NaN   \n",
       "\n",
       "   三边ND8% OK,COG侧ND5% OK  三边ND6% OK,COG侧ND5%OK  四边ND5% OK  ND5% NG 不良明细 显示指标  \n",
       "0                    1.0                   NaN        NaN      NaN  NaN   完成  \n",
       "1                    1.0                   NaN        NaN      NaN  NaN   完成  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['产品序号',\n",
       " 'ID',\n",
       " '编号',\n",
       " '三边无，COG侧ND5%OK',\n",
       " '三边ND8% OK,COG侧ND5% OK',\n",
       " '三边ND6% OK,COG侧ND5%OK',\n",
       " '四边ND5% OK',\n",
       " 'ND5% NG',\n",
       " '不良明细',\n",
       " '显示指标']"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_name = df1.columns.tolist()\n",
    "df1_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'不良明细'"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_name[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_ = np.sum(df1[df1_name[1]].notnull())\n",
    "ok_ = [0]*4\n",
    "for i in range(4):\n",
    "    ok_[i] = np.sum(df1[df1_name[3+i]].notnull())\n",
    "ng = np.sum(df1[df1_name[7]].notnull())\n",
    "buliang = np.sum(df1[df1_name[8]].notnull())\n",
    "bool_ = all_ == ok1 + ok2+ ok3+ ok4 + ng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 没有踢除 不良明细 时候的数据统计：\n",
      "        总数: 51 \n",
      "       三边无，COG侧ND5%OK: 10  \n",
      "        三边ND8% OK,COG侧ND5% OK: 40  \n",
      "       三边ND6% OK,COG侧ND5%OK: 0  \n",
      "        四边ND5% OK: 0   \n",
      "        ND5% NG: 0  \n",
      "        不良明细: 2 \n",
      "        数据是否正确： False \n",
      "      \n"
     ]
    }
   ],
   "source": [
    "print(f\" 没有踢除 不良明细 时候的数据统计：\\n \\\n",
    "       总数: {all_} \\n\\\n",
    "       {df1_name[3]}: {ok_[0]}  \\n \\\n",
    "       {df1_name[4]}: {ok_[1]}  \\n\\\n",
    "       {df1_name[5]}: {ok_[2]}  \\n \\\n",
    "       {df1_name[6]}: {ok_[3]}   \\n \\\n",
    "       {df1_name[7]}: {ng}  \\n \\\n",
    "       {df1_name[8]}: {buliang} \\n \\\n",
    "       数据是否正确： {bool_} \\n \\\n",
    "     \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "del_, okk_ = [0]*4, [0]*4\n",
    "for i in range(4):\n",
    "    del_[i] = np.sum(df1[df1_name[3+i]].notnull() & df1[df1_name[8]].notnull())\n",
    "    okk_[i] = ok_[i] - del_[i]\n",
    "all_2 = okk1 + okk2+ okk3+ okk4 + ng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0, 2, 0, 0], [10, 38, 0, 0])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del_,okk_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "del1 =  np.sum(df1[\"三边无，COG侧ND5%OK\"].notnull() & df1[\"不良明细\"].notnull())\n",
    "del2 = np.sum(df1[\"三边ND8% OK,COG侧ND5% OK\"].notnull() & df1[\"不良明细\"].notnull())\n",
    "del3 = np.sum(df1[\"三边ND6% OK,COG侧ND5%OK\"].notnull() & df1[\"不良明细\"].notnull())\n",
    "del4 = np.sum(df1[\"四边ND5% OK\"].notnull() & df1[\"不良明细\"].notnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "okk1 = ok1 - del1\n",
    "okk2 = ok2 - del2\n",
    "okk3 = ok3 - del3\n",
    "okk4 = ok4 - del4\n",
    "all_2 = okk1 + okk2+ okk3+ okk4 + ng"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        踢除 不良明细 时候的数据统计：\n",
      "        三边无，COG侧ND5%OK: 10  \n",
      "        三边ND8% OK,COG侧ND5% OK: 38 \n",
      "       三边ND6% OK,COG侧ND5%OK: 0 \n",
      "        四边ND5% OK: 0  \n",
      "      \n"
     ]
    }
   ],
   "source": [
    "print(f\" \\\n",
    "       踢除 不良明细 时候的数据统计：\\n \\\n",
    "       总数：all_2 \\n \\\n",
    "       三边无，COG侧ND5%OK: {okk1}  \\n \\\n",
    "       三边ND8% OK,COG侧ND5% OK: {okk2} \\n\\\n",
    "       三边ND6% OK,COG侧ND5%OK: {okk3} \\n \\\n",
    "       四边ND5% OK: {okk4}  \\n \\\n",
    "       \n",
    "     \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = [\"三边无，COG侧ND5%OK\",\"三边ND8% OK,COG侧ND5% OK\",\"三边ND6% OK,COG侧ND5%OK\",\"四边ND5% OK\", \"ND5% NG\", \"不良明细\", \"总数\",\"数据是否正确\"]\n",
    "col1 = [ok1, ok2, ok3, ok4, ng, buliang,all_, bool_]\n",
    "col2 = [okk1, okk2, okk3, okk4, ng, buliang, all_2, bool_]\n",
    "df2 = pd.DataFrame({\"统计类别\":name,\"没有踢除 不良明细\":col1, \"踢除 不良明细\":col2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.to_csv(\"./result/test.csv\",)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>统计类别</th>\n",
       "      <th>没有踢除 不良明细</th>\n",
       "      <th>踢除 不良明细</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>三边无，COG侧ND5%OK</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>三边ND8% OK,COG侧ND5% OK</td>\n",
       "      <td>40</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>三边ND6% OK,COG侧ND5%OK</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>四边ND5% OK</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ND5% NG</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>不良明细</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>总数</td>\n",
       "      <td>51</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>数据是否正确</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    统计类别 没有踢除 不良明细 踢除 不良明细\n",
       "0         三边无，COG侧ND5%OK        10      10\n",
       "1  三边ND8% OK,COG侧ND5% OK        40      38\n",
       "2   三边ND6% OK,COG侧ND5%OK         0       0\n",
       "3              四边ND5% OK         0       0\n",
       "4                ND5% NG         0       0\n",
       "5                   不良明细         2       2\n",
       "6                     总数        51      48\n",
       "7                 数据是否正确     False   False"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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